The modern communications era has brought about a tremendous expansion of wireline and wireless networks. Computer networks, television networks, and telephony networks are experiencing an unprecedented technological expansion, fueled by consumer demand. Wireless and mobile networking technologies have addressed related consumer demands, while providing more flexibility and immediacy of information transfer.
Current and future networking technologies continue to facilitate ease of information transfer and convenience to users. Due to the now ubiquitous nature of electronic communication devices, people of all ages and education levels are utilizing electronic devices to communicate with other individuals or contacts, receive services and/or share information, media and other content. One area in which there is a demand to increase ease of information transfer relates to three dimensional (3D) city modeling.
At present, reconstruction and display of 3D city models are generally important parts of next generation mapping systems, and 3D location and content services. Due to the number of constructions in an urban scene, manual modeling of urban objects may be expensive and time consuming. As such, automatic methods have been in high demand. Automatic computation of 3D city models has, in general, explored two different sources of data: (i) stereo photogrammetric imagery; and (ii) 3D sensors acquiring a 3D cloud of points. In this regard, some entities involved with city modeling have given the 3D city modeling field a new branch for realistic visualization. By collecting aerial imaging data, entities are able to produce detailed 3D views of urban scenes that may be composed of 3D meshes of triangles that are geo-referenced. Although realistic, such triangle meshes typically lack any information about objects in urban scenes, such as buildings, houses and the like. As such, at present, triangle meshes are very limited in usability and are used today as an isolated layer that serves only for visualization.
Automatic segmentation and modeling of urban objects from photogrammetry models may substantially increase the usability of triangle meshes by adding information that may allow for cross-referencing between different sources of data, reducing time and effort of specialized personnel. It may also increase coverage of current 3D maps for leveraging state-of-the-art photogrammetry modeling used by many location companies in today's market. However, at present, the broad academic research in the field indicates that the automatic generation of 3D city models may have been mainly based on low-throughput and expensive data collection, which may cause problems associated with limited coverage and extreme computational demand.
Current solutions to 3D segmentation and modeling of urban scenes may typically explore clouds of points created from specialized equipment such as laser sensors (e.g., Light Detection and Ranging (LiDAR)) installed on the bottom of an airborne entity (e.g., an airplane). In addition to being expensive, such specialized equipment has a very low throughput, which affects scalability over large portions of terrain. Referring to a dictionary of predefined shapes, modeling methods employ parametric approaches that optimize fitting between predefined models and the collected 3D data. For instance, shapes to be optimized may vary from low-level details such as lines and planes. At present, existing parametric approaches are limited to sensitive parameter setting and predefined shapes which typically require extreme computational demand. As such, at present, parametric approaches for reconstruction of 3D city models may be difficult to scale across cities with different architecture.
In view of the foregoing drawbacks, it may be desirable to provide an efficient and accurate mechanism for generating three dimensional geographical models based on photogrammetry imagery models.